title: Brain-machine interface control using broadband spectral power from local field potentials.
abstract: Recent progress in brain-machine interfaces (BMIs) has shown tremendous improvements in task complexity and degree of control. In particular, closed-loop decoder adaptation (CLDA) has emerged as an effective paradigm for both improving and maintaining the performance of BMI systems. Here, we demonstrate the first reported use of a CLDA algorithm to rapidly achieve high-performance control of a BMI based on local field potentials (LFPs). We trained a non-human primate to control a 2-D computer cursor by modulating LFP activity to perform a center-out reaching task, while applying CLDA to adaptively update the decoder. We show that the subject is quickly able to readily reach and hold at all 8 targets with an average success rate of 74% ± 7% (sustained peak rate of 85%), with rapid convergence in the decoder parameters. Moreover, the subject is able to maintain high performance across 4 days with minimal adaptations to the decoder. Our results indicate that CLDA can be used to facilitate LFP-based BMI systems, allowing for both rapid improvement and maintenance of performance.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Dangi
authors.forename: Kelvin
authors.initials: K
authors.lastname: So
authors.forename: Amy L
authors.initials: AL
authors.lastname: Orsborn
authors.forename: Michael C
authors.initials: MC
authors.lastname: Gastpar
authors.forename: Jose M
authors.initials: JM
authors.lastname: Carmena
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D062207:Brain-Computer Interfaces; D008253:Macaca mulatta / Q000502:physiology; D008297:Male; D013057:Spectrum Analysis